Search Results for author: Chao GAO

Found 50 papers, 5 papers with code

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

no code implementations21 Mar 2024 Zeyu Han, Chao GAO, Jinyang Liu, Jeff, Zhang, Sai Qian Zhang

In addition to the algorithmic perspective, we overview various real-world system designs to investigate the implementation costs associated with different PEFT algorithms.

GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion

no code implementations27 Feb 2024 Le Cheng, Peican Zhu, Keke Tang, Chao GAO, Zhen Wang

In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i. e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge.

GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking

no code implementations10 Dec 2023 Shu Yin, Chao GAO, Zhen Wang

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability.

Contrastive Learning Fake News Detection +1

Adaptive Multi-band Modulation for Robust and Low-complexity Faster-than-Nyquist Non-Orthogonal FDM IM-DD System

no code implementations6 Dec 2023 Peiji Song, Zhouyi Hu, Yizhan Dai, YuAn Liu, Chao GAO, Chun-Kit Chan

Faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) is robust against the steep frequency roll-off by saving signal bandwidth.

Computational Lower Bounds for Graphon Estimation via Low-degree Polynomials

no code implementations30 Aug 2023 Yuetian Luo, Chao GAO

From the statistical perspective, the minimax error rate of graphon estimation has been established by Gao et al (2015) for both stochastic block model (SBM) and nonparametric graphon estimation.

Community Detection Graphon Estimation +1

Sequential Attention Source Identification Based on Feature Representation

no code implementations28 Jun 2023 Dongpeng Hou, Zhen Wang, Chao GAO, Xuelong Li

Snapshot observation based source localization has been widely studied due to its accessibility and low cost.

Graph Attention

Minimax Signal Detection in Sparse Additive Models

no code implementations19 Apr 2023 Subhodh Kotekal, Chao GAO

Sparse additive models are an attractive choice in circumstances calling for modelling flexibility in the face of high dimensionality.

Additive models

PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting

no code implementations ICCV 2023 Xin Deng, Chao GAO, Mai Xu

In this paper, we propose a novel method namely PIRNet, which operates privacy-preserving image restoration in the steganographic domain.

Deblurring Image Denoising +3

Uncertainty quantification in the Bradley-Terry-Luce model

no code implementations8 Oct 2021 Chao GAO, Yandi Shen, Anderson Y. Zhang

The Bradley-Terry-Luce (BTL) model is a benchmark model for pairwise comparisons between individuals.

Uncertainty Quantification

Optimal Orthogonal Group Synchronization and Rotation Group Synchronization

no code implementations28 Sep 2021 Chao GAO, Anderson Y. Zhang

We study the statistical estimation problem of orthogonal group synchronization and rotation group synchronization.

Asymptotic analysis of V-BLAST MIMO for coherent optical wireless communications in Gamma-Gamma turbulence

no code implementations12 Jul 2021 Yiming Li, Chao GAO, Mark S. Leeson, Xiaofeng Li

This paper investigates the asymptotic BER performance of coherent optical wireless communication systems in Gamma-Gamma turbulence when applying the V-BLAST MIMO scheme.

On AO*, Proof Number Search and Minimax Search

no code implementations30 Mar 2021 Chao GAO

We discuss the interconnections between AO*, adversarial game-searching algorithms, e. g., proof number search and minimax search.

On Computation Complexity of True Proof Number Search

no code implementations8 Feb 2021 Chao GAO

We point out that the computation of true \emph{proof} and \emph{disproof} numbers for proof number search in arbitrary directed acyclic graphs is NP-hard, an important theoretical result for proof number search.

Optimal Full Ranking from Pairwise Comparisons

no code implementations21 Jan 2021 Pinhan Chen, Chao GAO, Anderson Y. Zhang

We consider the problem of ranking $n$ players from partial pairwise comparison data under the Bradley-Terry-Luce model.

SDP Achieves Exact Minimax Optimality in Phase Synchronization

no code implementations7 Jan 2021 Chao GAO, Anderson Y. Zhang

We study the phase synchronization problem with noisy measurements $Y=z^*z^{*H}+\sigma W\in\mathbb{C}^{n\times n}$, where $z^*$ is an $n$-dimensional complex unit-modulus vector and $W$ is a complex-valued Gaussian random matrix.

Residual Matrix Product State for Machine Learning

no code implementations22 Dec 2020 Ye-Ming Meng, Jing Zhang, Peng Zhang, Chao GAO, Shi-Ju Ran

Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning.

BIG-bench Machine Learning Quantum Machine Learning +1

Convergence Rates of Empirical Bayes Posterior Distributions: A Variational Perspective

no code implementations8 Sep 2020 Fengshuo Zhang, Chao GAO

We study the convergence rates of empirical Bayes posterior distributions for nonparametric and high-dimensional inference.

Density Estimation

Partial Recovery for Top-$k$ Ranking: Optimality of MLE and Sub-Optimality of Spectral Method

no code implementations30 Jun 2020 Pinhan Chen, Chao GAO, Anderson Y. Zhang

We also derive the optimal signal to noise ratio condition for the exact recovery of the top-$k$ set.

Model Repair: Robust Recovery of Over-Parameterized Statistical Models

no code implementations20 May 2020 Chao Gao, John Lafferty

A new type of robust estimation problem is introduced where the goal is to recover a statistical model that has been corrupted after it has been estimated from data.

LEMMA

Iterative Algorithm for Discrete Structure Recovery

no code implementations4 Nov 2019 Chao Gao, Anderson Y. Zhang

We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems.

Clustering

Three-Head Neural Network Architecture for AlphaZero Learning

no code implementations25 Sep 2019 Chao GAO, Martin Mueller, Ryan Hayward, Hengshuai Yao, Shangling Jui

A three-head network architecture has been recently proposed that can learn a third action-value head on a fixed dataset the same as for two-head net.

A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification

no code implementations17 Aug 2019 Yang Liu, Jianpeng Zhang, Chao GAO, Jinghua Qu, Lixin Ji

In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e. g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function.

General Classification Sentence +1

Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks

no code implementations10 Aug 2019 Yang Liu, Jianpeng Zhang, Chao GAO, Jinghua Qu, Lixin Ji

Activation functions play a key role in providing remarkable performance in deep neural networks, and the rectified linear unit (ReLU) is one of the most widely used activation functions.

On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

no code implementations26 Jul 2019 Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman.

reinforcement-learning Reinforcement Learning (RL)

ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS

no code implementations ICLR 2019 Chao GAO, jiyi LIU, Yuan YAO, Weizhi Zhu

In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model.

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

1 code implementation20 Apr 2019 Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.

Reinforcement Learning (RL)

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

no code implementations10 Apr 2019 Bilal Kartal, Pablo Hernandez-Leal, Chao GAO, Matthew E. Taylor

In this paper, we shed light into the reasons behind this failure by exemplifying and analyzing the high rate of catastrophic events (i. e., suicides) that happen under random exploration in this domain.

reinforcement-learning Reinforcement Learning (RL) +1

Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective

1 code implementation5 Mar 2019 Chao Gao, Yuan YAO, Weizhi Zhu

Robust scatter estimation is a fundamental task in statistics.

Continual Match Based Training in Pommerman: Technical Report

no code implementations18 Dec 2018 Peng Peng, Liang Pang, Yufeng Yuan, Chao GAO

We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones.

Continual Learning

Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing

no code implementations14 Nov 2018 Chao Gao, Zongming Ma

This paper surveys some recent developments in fundamental limits and optimal algorithms for network analysis.

Community Detection Graphon Estimation +1

Robust Estimation and Generative Adversarial Nets

2 code implementations4 Oct 2018 Chao Gao, jiyi LIU, Yuan YAO, Weizhi Zhu

Similar to the derivation of $f$-GANs, we show that these depth functions that lead to statistically optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of $f$-Learning.

IoT Security: An End-to-End View and Case Study

no code implementations15 May 2018 Zhen Ling, Kaizheng Liu, Yiling Xu, Chao GAO, Yier Jin, Cliff Zou, Xinwen Fu, Wei Zhao

The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.

Cryptography and Security

N-fold Superposition: Improving Neural Networks by Reducing the Noise in Feature Maps

no code implementations23 Apr 2018 Yang Liu, Qiang Qu, Chao GAO

Finally, we replicate this new block into n copies and concatenate them as the input to the FC layer.

LEMMA

Adversarial Policy Gradient for Alternating Markov Games

no code implementations ICLR 2018 Chao Gao, Martin Mueller, Ryan Hayward

As policy gradient method is a kind of generalized policy iteration, we show how these differences in policy iteration are reflected in policy gradient for AMGs.

Policy Gradient Methods

Convergence Rates of Variational Posterior Distributions

no code implementations7 Dec 2017 Fengshuo Zhang, Chao GAO

For a class of priors that admit the structure of a mixture of product measures, we propose a novel prior mass condition, under which the variational approximation error of the mean-field class is dominated by convergence rate of the true posterior.

Phase Transitions in Approximate Ranking

no code implementations30 Nov 2017 Chao Gao

We study the problem of approximate ranking from observations of pairwise interactions.

Testing for Global Network Structure Using Small Subgraph Statistics

no code implementations2 Oct 2017 Chao Gao, John Lafferty

We study the problem of testing for community structure in networks using relations between the observed frequencies of small subgraphs.

Methodology Social and Information Networks Statistics Theory Applications Statistics Theory

Stochastic Canonical Correlation Analysis

no code implementations21 Feb 2017 Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang

We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbitrarily small error.

Stochastic Optimization

Robust Regression via Mutivariate Regression Depth

no code implementations15 Feb 2017 Chao Gao

This paper studies robust regression in the settings of Huber's $\epsilon$-contamination models.

regression

Exact Exponent in Optimal Rates for Crowdsourcing

no code implementations25 May 2016 Chao Gao, Yu Lu, Dengyong Zhou

In many machine learning applications, crowdsourcing has become the primary means for label collection.

Optimal Estimation and Completion of Matrices with Biclustering Structures

no code implementations1 Dec 2015 Chao Gao, Yu Lu, Zongming Ma, Harrison H. Zhou

Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (1972) where one seeks to cluster cases and variables simultaneously.

Sparse CCA via Precision Adjusted Iterative Thresholding

no code implementations24 Nov 2013 Mengjie Chen, Chao GAO, Zhao Ren, Harrison H. Zhou

Sparse Canonical Correlation Analysis (CCA) has received considerable attention in high-dimensional data analysis to study the relationship between two sets of random variables.

Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels

no code implementations22 Oct 2013 Chao Gao, Dengyong Zhou

Crowdsourcing has become a primary means for label collection in many real-world machine learning applications.

Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes

no code implementations31 Jul 2013 Mengjie Chen, Chao GAO, Hongyu Zhao

The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed.

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